Automation in bio/pharmaceutical manufacturing offers a reduction in human intervention, which can enhance quality in a highly regulated environment. Downstream processing consists of product recovery and purification. “The process is complex and requires precise control of process parameters, such as temperature, pH, dissolved oxygen, and nutrient levels, to ensure product quality” (1).
The use of automation downstream, such as smart instrumentation, flexible network architectures, and a robust ethernet-based databus, may help streamline processing compared to traditional downstream processes, according to Cory Perelman, PE, lead I&C Engineer and ASME BPE PI vice chair at Arcadis.Smart analytics, says Perelman, results in more informed decisions and reduced downtime. “With smart-instrumentation, calibration cycles can be data-based and not as risk-based, [for example], re-calibrating when an instrument tells you, and not at the beginning of each batch. pH probe change-outs can be data-driven, flowmeter calibration can be data-driven, and radars (both guided-wave and through-air) can be left in tanks indefinitely. With an ethernet-databus, all smart instrument and valve data [are] available to be displayed, trended, and ultimately, drive SOPs [standard operating procedures],” says Perelman. “This all helps move away from traditional batch downstream processes and push us towards continuous, where there are smaller footprints both in size and utility constraints.”
The use of continuous processes is growing in biopharmaceutical manufacturing, by intensifying production, lowering costs, and enabling enhanced control (e.g., continuous chromatography being used in the purification of protein therapeutics) (2). Automation and robot handling also enable the use of closed systems, which minimize the risk of manual intervention (3). Automation may also reduce the risk of transfer error that may happen when a human enters or reads information (3).
Enhanced quality and control
“Automation plays a critical role in enhancing control over processes, including downstream processing,” says Edita Botonjic-Sehic, head of Process Analytics and Data Science at ReciBioPharm. “With fully automated and integrated control capabilities from end-to-end of the bioprocess, operators can rapidly detect any deviation in either the process or the product. This allows drug developers and manufacturers to act quickly to mitigate the impact of these deviations, saving time, reducing batch failures, and improving overall product quality and consistency at every scale.”
Productivity and quality may be improved with the use of automation in downstream processing, according to Ryan Thompson, senior specialist, Industry 4.0, CRB. Automation has the ability to characterize processes and implement process verification, which Thompson specifies is crucial in implementing process analytical technologies.
Monitoring data is an area automation can be used to the fullest, according to Botonjic-Sehic. “Throughout process characterization, development, and manufacturing, data monitoring is critical for identifying the correct critical quality attributes (CQAs) and process parameters supporting optimal production and performance,” says Botonjic-Sehic. “Incorporating automation effectively facilitates the integration of all process and analytical systems, helping to promote consistency and enhance data integrity, resulting in higher-quality data. By minimizing delays caused by poor decision-making led by erroneous data, developers and manufacturers can deliver essential therapies to patients faster.”
Sustainability, which is of growing interest for the bio/pharmaceutical industry, may be achieved by using automated single-use bioprocessing (1). Because single-use systems do not require cleaning processes, they can reduce the environmental impact by saving water and energy (1).
Automated technologies downstream
Data collection and analysis downstream can be controlled by implementing a framework for controlling equipment and analytical instruments, remarks Botonjic-Sehic. Data can be fed into developed chemometric models for continuous modeling and control. A human-machine interface (HMI) can be used to monitor processes and allow an operator to intervene if needed, “ensuring optimum process control and product quality,” according to Botonjic-Sehic. “Automated documentation of critical process parameters and CQAs can improve the efficiency of process maintenance. [And a] chemometric model of each unit operation can improve productivity at each step.”
“Choosing technology platforms that are open and interoperable with other systems is a key consideration for modern automation systems. This ensures equipment can be easily integrated into a facility, no technical debt is accumulated, and fit-for-purpose tools can be selected instead of being locked into one vendor ecosystem,” says Thompson. “By leveraging the data created by automation systems, processes can be improved through traditional design of experiments or through insights from advanced analytics or machine learning tools.”
Recent advancements in automation of downstream processing include single-use technology in downstream processes, inline conditioning for chromatography, and continuous chromatography, according to David Sheedy, head of Life Sciences Manufacturing Ireland, Cognizant. “As with all these process technology enhancements, automation is evolving by creating new digital solutions to support these needs, which leads to end-to-end, fully automated batch process control,” says Sheedy.
The use of multi-column chromatography, asserts Emily Heffernan, US director New Process Technology, Arcadis, has greatly enhanced downstream processing. “Through the use of automation, chromatography column use is optimized with product diverted from the initial column to a subsequent one when product breakthrough occurs. This leads to a reduction in chromatography resin, buffer volume, and overall cost,” she says.
The use of artificial intelligence (AI) in bio/pharmaceutical processes does come with some risks, as Adam Fisher, director of Science Staff and Immediate Office within the Office of Pharmaceutical Quality at the Center for Drug Evaluation and Research at FDA, pointed out in an article in Pharmaceutical Technology. “Access to high-quality data is a fundamental requirement for effective AI training or learning. AI can be particularly sensitive to the characteristics of the data used for training, testing, and validation. The process analytical technologies providing data to AI systems must be accurate and representative. For learning purposes, data must represent not only process successes but also process failures. It will be critical to ensure that data used for AI training or learning are fit for use based on quality, reliability, and representativeness” (4).
References
- Muhlegger, M. Automated Bioprocessing–7 Advantages of Automation. Single Use Support. April 12, 2023. https://www.susupport.com/knowledge/single-use-technology/automated-bioprocessing-advantages-automation
- Mirasol, F. Evaluating the Use of Continuous Chromatography. BioPharm International 2024 37 (6). https://www.biopharminternational.com/view/evaluating-the-use-of-continuous-chromatography
- Markarian, J. Streamlining Downstream Processes. BioPharm International 2023 36 (8). https://www.biopharminternational.com/view/streamlining-downstream-processes
- Fisher, A. The Future is the Present: Artificial Intelligence in Pharmaceutical Manufacturing. Pharmaceutical Technology 2023 47 (9). https://www.pharmtech.com/view/the-future-is-the-present-artificial-intelligence-in-pharmaceutical-manufacturing
About the author
Susan Haigney is lead editor of BIoPharm International®.